Profiling of the effects of antifungal agents on yeast cells based on morphometric analysis.

The incidence of fungal infection and evolution of multidrug resistance have increased the need for new antifungal agents. To gain further insight into the development of antifungal drugs, the phenotypic profiles of currently available antifungal agents of three classes-ergosterol, cell wall and nucleic acid biosynthesis inhibitors-were investigated using yeast morphology as a chemogenomic signature. The comparison of drug-induced morphological changes with the deletion of 4718 non-essential genes not only confirmed the mode of action of the drugs but also revealed an unexpected connection among ergosterol, vacuolar proton-transporting V-type ATPase and cell-wall-targeting drugs. To improve, simplify and accelerate drug development, we developed a systematic classifier that sorts a newly discovered compound into a class with a similar mode of action without any mutant information. Using well-characterized agents as target unknown compounds, this method successfully categorized these compounds into their respective classes. Based on our data, we suggest that morphological profiling can be used to develop novel antifungal drugs.

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.

Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.